Blog

Research, releases, and perspectives from the Zen LM team.

Introducing Qwen-VL

Along with the rapid development of our large language model Qwen, we leveraged Qwen’s capabilities and unified multimodal pretraining to address the limitations of multimodal models in generalization, and we opensourced multimodal model Qwen-VL in Sep. 2023. Recently, the Qwen-VL series has undergone a significant upgrade with the launch of two enhanced versions, Qwen-VL-Plus and Qwen-VL-Max. The key technical advancements in these versions include:

Zen LM Team

OFASys: Enabling Multitask Learning with One Line of Code!

Intro Generalist Models are hot! We all see an opportunity towards a real generalist model by multimodal multitask learning. We previously release an opensourced unified multimodal pretrained model OFA for this goal. However, we actually met a lot of difficulties in our implementation. For example, it is hard to set up multiple tasks concerning multiple modalities, and it is hard to organize multitask learning, e.g., how to batchify your data and how to make your training stable.

Zen LM Team

Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese

CLIP1 is a phenomenal playmaker in vision and multimodal representation learning. It plays not only as a foundation model but also a bridge between vision and language. It has triggered a series of research in different fields, especially text-to-image generation. However, we find that there is a necessity for a language-specific CLIP for applications, especially cross-modal retrieval, and there is no opensourced Chinese CLIP with good performance. We therefore launched this project to promote the Chinese multimodal representation learning.

Zen LM Team

OFA: Towards Building a One-For-All Model

2022 is a year of generalist models! With the bloom of multimodal pretraining, especially the unified model, we have witnessed the opportunity to building a generalist model that is capable of processing tasks of different modalities or multi-modalities! Thus, we propose OFA1, namely One-For-All, a unified multimodal pretrained model that unifies understanding and generation tasks concerning modalities into a single framework, and we pretrain OFA with the instruction-based multitask-pretraining that endows it with multiple capabilities.

Zen LM Team